/
test_utils.py
179 lines (159 loc) · 5.69 KB
/
test_utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import sklearn.model_selection
import sklearn.preprocessing
import pycaret.classification
import pycaret.datasets
import pycaret.regression
import pycaret.utils
from pycaret.utils.constants import LABEL_COLUMN
from pycaret.utils.generic import check_metric
def test_utils():
# version
version = pycaret.utils.version()
assert isinstance(version, str)
version = pycaret.utils.__version__
assert isinstance(version, str)
# preparation(classification)
data = pycaret.datasets.get_data("juice")
train, test = sklearn.model_selection.train_test_split(
data, train_size=0.8, random_state=1
)
clf1 = pycaret.classification.setup(
train,
target="Purchase",
html=False,
session_id=123,
n_jobs=1,
)
model = pycaret.classification.create_model("lightgbm")
final_model = pycaret.classification.finalize_model(model)
result = pycaret.classification.predict_model(
final_model, data=test.drop("Purchase", axis=1), encoded_labels=True
)
actual = clf1.pipeline.transform(y=test["Purchase"])
prediction = result[LABEL_COLUMN]
# provisional support
actual = actual.dropna(axis=0, how="any")
actual = actual.reset_index()
actual = actual["Purchase"].astype(np.int64)
prediction = prediction.dropna(axis=0, how="any")
prediction = prediction.reset_index()
prediction = prediction[LABEL_COLUMN].astype(np.int64)
# check metric(classification)
accuracy = check_metric(actual, prediction, "Accuracy")
assert isinstance(accuracy, float)
assert accuracy >= 0
assert accuracy <= 1
recall = check_metric(actual, prediction, "Recall")
assert isinstance(recall, float)
assert recall >= 0
assert recall <= 1
precision = check_metric(actual, prediction, "Precision")
assert isinstance(precision, float)
assert precision >= 0
assert precision <= 1
f1 = check_metric(actual, prediction, "F1")
assert isinstance(f1, float)
assert f1 >= 0
assert f1 <= 1
kappa = check_metric(actual, prediction, "Kappa")
assert isinstance(kappa, float)
assert kappa >= -1
assert kappa <= 1
auc = check_metric(actual, prediction, "AUC")
assert isinstance(auc, float)
assert auc >= 0
assert auc <= 1
mcc = check_metric(actual, prediction, "MCC")
assert isinstance(mcc, float)
assert mcc >= -1
assert mcc <= 1
# preparation(regression)
data = pycaret.datasets.get_data("boston")
train, test = sklearn.model_selection.train_test_split(
data, train_size=0.8, random_state=1
)
pycaret.regression.setup(
data,
target="medv",
html=False,
session_id=123,
n_jobs=1,
)
model = pycaret.regression.create_model("lightgbm")
final_model = pycaret.regression.finalize_model(model)
result = pycaret.regression.predict_model(
final_model, data=test.drop("medv", axis=1)
)
actual = test["medv"]
prediction = result[LABEL_COLUMN]
# provisional support
actual = actual.dropna(axis=0, how="any")
actual = actual.reset_index()
actual = actual.drop("index", axis=1)
prediction = prediction.dropna(axis=0, how="any")
prediction = prediction.reset_index()
prediction = prediction.drop("index", axis=1)
# check metric(regression)
mae = check_metric(actual, prediction, "MAE")
assert isinstance(mae, float)
assert mae >= 0
mse = check_metric(actual, prediction, "MSE")
assert isinstance(mse, float)
assert mse >= 0
rmse = check_metric(actual, prediction, "RMSE")
assert isinstance(rmse, float)
assert rmse >= 0
r2 = check_metric(actual, prediction, "R2")
assert isinstance(r2, float)
assert r2 <= 1
rmsle = check_metric(actual, prediction, "RMSLE")
assert isinstance(rmsle, float)
assert rmsle >= 0
mape = check_metric(actual, prediction, "MAPE")
assert isinstance(mape, float)
assert mape >= 0
# Ensure metric is rounded to 2 decimals
mape = check_metric(actual, prediction, "MAPE", 2)
npt.assert_almost_equal(mape, 0.05, decimal=2)
# Ensure metric is rounded to default value
mape = check_metric(actual, prediction, "MAPE")
npt.assert_almost_equal(mape, 0.045, decimal=2)
# preparation (timeseries)
data = pycaret.datasets.get_data("airline", verbose=False)
train, test = sklearn.model_selection.train_test_split(
data, train_size=0.8, random_state=1, shuffle=False
)
prediction = pd.Series([100] * len(test), index=test.index)
actual = test
# check metric(timeseries)
smape = check_metric(actual, prediction, "SMAPE")
assert isinstance(smape, float)
assert smape >= 0
mape = check_metric(actual, prediction, "MAPE")
assert isinstance(mape, float)
assert mape >= 0
# mase = pycaret.utils.check_metric(test, prediction, "MASE", train=train)
# assert isinstance(mase, float)
# assert mase >= 0
mae = check_metric(actual, prediction, "MAE")
assert isinstance(mae, float)
assert mae >= 0
rmse = check_metric(actual, prediction, "RMSE")
assert isinstance(rmse, float)
assert rmse >= 0
# Ensure metric is rounded to 2 decimals
smape = check_metric(actual, prediction, "SMAPE", 2)
npt.assert_almost_equal(smape, 1.24, decimal=2)
# Ensure metric is rounded to default value
smape = check_metric(actual, prediction, "SMAPE")
npt.assert_almost_equal(smape, 1.2448, decimal=4)
# Metric does not exist
with pytest.raises(ValueError, match="Couldn't find metric"):
check_metric(actual, prediction, "INEXISTENTMETRIC")
assert 1 == 1
if __name__ == "__main__":
test_utils()